With the advancements of the computational resources, many Deep Neural Networks (DNNs) architectures and networks have been proposed to make speech recognition more efficient and accurate. DNN-HMM hybrid systems [1]} outperform conventional GMM-HMM systems. For end-to-end (e2e) speech recognition, sequence-to-sequence models [2]}, RNN transducers (RNN-T) [3]}, transformers [4]} and unsupervised learning [5]} are being used. These systems can be further improved with coupling of various techniques such as multi-task learning (MTL) [6]}, mixture of experts (MOE) [7]} and learning hidden unit contributions (LHUC) [8]} depending on the task. All these statistical modelling techniques require a lot of data for reliable parameters estimation. However, out of nearly 7000 languages being spoken around the world, just 23 languages are spoken by more than half of the world's population [9]}. So, sufficient data resources are available for few languages.
